CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene

TL;DR

CROON is an automatic, two-stage calibration method for multiple LiDAR sensors in road scenes, enhancing perception accuracy for autonomous vehicles through robust and scalable calibration techniques.

Contribution

The paper introduces CROON, a novel two-stage calibration approach that automatically and precisely calibrates multiple LiDARs from arbitrary initial poses in road environments.

Findings

Demonstrates high accuracy on real-world and simulated data

Independent of initial pose, robust in large-scale scenarios

Open-source code and datasets provided

Abstract

Sensor-based environmental perception is a crucial part of the autonomous driving system. In order to get an excellent perception of the surrounding environment, an intelligent system would configure multiple LiDARs (3D Light Detection and Ranging) to cover the distant and near space of the car. The precision of perception relies on the quality of sensor calibration. This research aims at developing an accurate, automatic, and robust calibration strategy for multiple LiDAR systems in the general road scene. We thus propose CROON (automatiC multi-LiDAR CalibratiOn and Refinement method in rOad sceNe), a two-stage method including rough and refinement calibration. The first stage can calibrate the sensor from an arbitrary initial pose, and the second stage is able to precisely calibrate the sensor iteratively. Specifically, CROON utilize the nature characteristics of road scene so that it is independent and easy to apply in large-scale conditions. Experimental results on real-world and simulated data sets demonstrate the reliability and accuracy of our method. All the related data sets and codes are open-sourced on the Github website https://github.com/OpenCalib/LiDAR2LiDAR.